Remission of depression in patients with schizophrenia and comorbid major depressive disorder: results from the FACE-SZ cohort
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is underdiagnosed and undertreated in schizophrenia, and has been strongly associated with impaired quality of life.AimsTo determine the prevalence and associated factors of MDD and unremitted MDD in schizophrenia, to compare treated and non-treated MDD. METHOD: Participants were included in the FondaMental Expert Centers for Schizophrenia and received a thorough clinical assessment. MDD was defined by a Calgary score ≥6. Non-remitted MDD was defined by current antidepressant treatment (unchanged for >8 weeks) and current Calgary score ≥6. RESULTS: 613 patients were included and 175 (28.5%) were identified with current MDD. MDD has been significantly associated with respectively paranoid delusion (odds ratio 1.8; P = 0.01), avolition (odds ratio 1.8; P = 0.02), blunted affect (odds ratio 1.7; P = 0.04) and benzodiazepine consumption (odds ratio 1.8; P = 0.02). Antidepressants were associated with lower depressive symptoms score (5.4 v. 9.5; P < 0.0001); however, 44.1% of treated patients remained in non-remittance MDD. Nonremitters were found to have more paranoid delusion (odds ratio 2.3; P = 0.009) and more current alcohol misuse disorder (odds ratio 4.8; P = 0.04). No antidepressant class or specific antipsychotic were associated with higher or lower response to antidepressant treatment. MDD was associated with Metabolic syndrome (31.4 v. 20.2%; P = 0.006) but not with increased C-reactive protein. CONCLUSIONS: Antidepressant administration is associated with lower depressive symptom level in patients with schizophrenia and MDD. Paranoid delusions and alcohol misuse disorder should be specifically explored and treated in cases of non-remission under treatment. MetS may play a role in MDD onset and/or maintenance in patients with schizophrenia.Declaration of interestNone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".